Get the necessary packages

First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.

## Warning: package 'tidyverse' was built under R version 4.0.5

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.4     v dplyr   1.0.7
## v tidyr   1.1.4     v stringr 1.4.0
## v readr   2.0.2     v forcats 0.5.1

## Warning: package 'ggplot2' was built under R version 4.0.5

## Warning: package 'tibble' was built under R version 4.0.5

## Warning: package 'tidyr' was built under R version 4.0.5

## Warning: package 'readr' was built under R version 4.0.5

## Warning: package 'dplyr' was built under R version 4.0.5

## Warning: package 'forcats' was built under R version 4.0.5

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

## Warning: package 'gganimate' was built under R version 4.0.5

## Warning: package 'gifski' was built under R version 4.0.5

## Warning: package 'av' was built under R version 4.0.5

## Warning: package 'gapminder' was built under R version 4.0.5

Look at the data and tackle the tasks

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

str(gapminder)

## tibble [1,704 x 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...

unique(gapminder$year)

##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007

head(gapminder)

## # A tibble: 6 x 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility

ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() 

…

We see an interesting spread with an outlier to the right. Answer the following questions, please:

1. Why does it make sense to have a log10 scale on x axis?

When we have such a big pool of data that ranges over several orders of magnitude on the x-axis it helps to use log10 scale, because it allows a larger range of values to be shown without the smallest becoming pressed down in the bottom.

2. Who is the outlier (the richest country in 1952 - far right on x axis)?

I am creating an object that contains the variables country and gdpPercap in 1952 arranged in a descending order, so that the richest countries are placed first in the tibble. To get it to print only the country with the highest gdpPercap I subset ‘[]’ the previously made tibble:

gapminder_year_1952 <-  gapminder %>% 
  filter(year == 1952) %>% 
  select(country, gdpPercap) %>% 
  arrange(desc(gdpPercap))

gapminder_year_1952[1, 1:2]

## # A tibble: 1 x 2
##   country gdpPercap
##   <fct>       <dbl>
## 1 Kuwait    108382.

Next, you can generate a similar plot for 2007 and compare the differences

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() 

…

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Tasks:

3. Differentiate the continents by color, and fix the axis labels and units to be more legible (Hint: the 2.50e+08 is so called “scientific notation”, which you might want to eliminate)

To easily differentiated the continent by colour, I, further specified the ggplot to change the color for the different continents within the aesthetic (aes) function:

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point() +
  scale_x_log10()

To fix the axis labels and units I used the labs function. Within the function I specified it to change the titles of the labels of the x-and y-axis and the size and colour of the font for the different units and labels. Finally, I removed the ‘scientific notation’:

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point() +
  scale_x_log10() +
  labs(x = "gdp per capita", y = "Life expectancy") +
  theme(axis.text.x = element_text(colour = "grey20", size = 12), axis.text.y = element_text(colour = "grey20", size = 12), text = element_text(size = 14))

options(scipen = 10)

4. What are the five richest countries in the world in 2007?

To find the 5 richest countries in 2007 I created an object that contains the data from the variables country and gdpPercap in 2007 arranged in a descending order, so that the richest countries are placed first in the tibble. To get it to only print the 5 richest countries I used the head function on the created object (tibble):

gapminder_2007 <- gapminder %>% 
  filter(year == 2007) %>% 
  select(country, gdpPercap) %>% 
  arrange(desc(gdpPercap))

head(gapminder_2007)  

## # A tibble: 6 x 2
##   country          gdpPercap
##   <fct>                <dbl>
## 1 Norway              49357.
## 2 Kuwait              47307.
## 3 Singapore           47143.
## 4 United States       42952.
## 5 Ireland             40676.
## 6 Hong Kong, China    39725.

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.

Also, there are two ways of animating the gapminder ggplot.

anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()  # convert x to log scale
anim

anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1)

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() + # convert x to log scale
  transition_time(year)
anim2

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() +
  transition_time(year) + 
  labs(title = "year: {frame_time} ")

anim2

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) + 
  geom_point() +
  scale_x_log10() +
  transition_time(year) + 
  labs(title = "year: {frame_time} ", x = "gdp per capita", y = "Life expectancy") +
  theme(axis.text.x = element_text(colour = "grey20", size = 12), axis.text.y = element_text(colour = "grey20", size = 12), text = element_text(size = 14))

anim2